Mining Actor-level Structural and Neighborhood Evolution for Link Prediction in Dynamic Networks

Abstract

Link prediction problem in network science has experienced extensive methodological improvements and simultaneously, spawned over numerous applications. In relation to evolutionary network analysis, different dynamic link prediction methods in network science not only support the prediction of future links but also assist in modelling network dynamics. The concept of constructing dynamic similarity metrics by considering the actor-level evolution of network structure and associated neighborhoods has been widely ignored for the purpose of dynamic link prediction. This study attempts to propose two dynamic similarity metrics for the purpose of dynamic link prediction in longitudinal networks through mining evolutionary information. These metrics consider the similarity between network structural and neighborhood changes over time incident to non-connected actor pairs. These metrics are then used as dynamic features in supervised link prediction model and performances are compared against two baseline static similarity metrics (i.e., AdamicAdar and Katz). Higher performance scores achieved by these features, examined in this study, exemplifies them as prospective candidates not only for dynamic link prediction task but also in understanding the growth pattern of dynamic networks.